WPS4616
Policy ReseaRch WoRking PaPeR 4616
Migration, Sorting and Regional Inequality:
Evidence from Bangladesh
Forhad Shilpi
The World Bank
Development Research Group
Sustainable Rural and Urban Development Team
May 2008
Policy ReseaRch WoRking PaPeR 4616
Abstract
Using household level data from Bangladesh, this are higher. Within each region, mobility of workers seems
paper examines the differences in the rates of return to to equalize returns at the lower half of the distribution.
household attributes over the entire welfare distribution. The natural border created by the rivers appears to
The empirical evidence uncovers substantial differences hinder migration, causing returns differences between
in returns between an integrated region contiguous to the regions to persist. To reduce regional inequality in
the country's main growth centers, and a less integrated welfare in Bangladesh, the results highlight the need for
region cut-off from those centers by major rivers. The improving connectivity between the regions, and for
evidence suggests that households with better observed investing in portable assets of the poor (such as human
and unobserved attributes (such as education and ability) capital).
are concentrated in the integrated region where returns
This paper--a product of the Sustainable Rural and Urban Development Team, Development Research Group--is part of
a larger effort in the department to understand the implications of migration and access to market for regional inequality
in living standards. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author
may be contacted at fshilpi@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Migration, Sorting and Regional Inequality:
Evidence from Bangladesh
Forhad Shilpi*
World Bank
JEL Classification: O18; O53; C15
Key Words: Unobserved Heterogeneity, Sorting, Migration, Quantile Decomposition
*Correspondence contact: F. Shilpi, MSN MC3-300, World Bank, 1818H Street NW, Washington DC
20433, Phone: (202) 458- 7476, Fax: (202) 522 -1151, email: fshilpi@worldbank.org.
The views expressed here are those of authors and should not be attributed to World Bank or its
affiliates.
1 Introduction
In recent years, spatial inequality in living standards has become an important policy issue
in many developing countries. Numerous empirical studies have shown that households with
attributes that perpetuate poverty tend to concentrate in poor areas -- areas characterized by
poor infrastructure and amenities, and by lack of access to markets (Kanbur and Venables,
2005; Jalan and Ravallion, 2002). More importantly, rates of return to observable household
attributes vary across regions even in countries with no apparent restriction on migration. In
this study, we examine the differences in living standards across regions with different levels
of infrastructure development focusing specifically on the differences in returns to observed
household attributes. Instead of examining only the mean differences, we analyze the differ-
ences in returns over the entire distribution of real per capita household expenditure. The
analysis of the spatial gaps in returns over the entire income distribution can shed light into
the relative importance of different factors that may cause these gaps to persist.
Existing literature offers two broad explanations for the persistence of the spatial gaps in
returns even with free factor mobility. First, in econometric estimation, return to the same
household attribute can be found to differ significantly across locations if the heterogeneity
across households and locations is not adequately controlled for. At least three such sources
of unobserved household and locational heterogeneity can be discerned from the existing lit-
erature. According to the standard locational sorting model a la Roy (1951), households are
sorted across regions in terms of both observed and unobserved characteristics. For instance,
while educational attainment is observed, the ability of an individual is unobservable. The
selective migration of workers with better ability to urban areas means that an individual in
an urban area will earn a higher wage compared with an observationally identical individ-
ual located in a rural area. In addition to ability sorting, agglomeration economies arising
from increasing returns, thick labor market externalities and knowledge spill-overs can cause
wages in densely populated areas and in technologically advanced sectors to be higher (Fujita,
Krugman and Venables, 1999; Overman, Rice and Venables, 2007). Moreover, if public in-
frastructure has a positive production externality, then workers in regions with better access
1
to markets and better infrastructure could enjoy higher wages relative to those located in
other regions (Ravallion and Jalan, 1999; Jalan and Ravallion, 2002). The omitted variable
biases resulting from the inability to control for the spatial sorting of unobserved attributes
do not, however, apply to all households and all locations equally. The ability sorting and
agglomeration economies may affect the wages in sectors which are technology and innovation
intensive. Evidence from developing countries suggests that only a small fraction of activities
in urban centers qualify for such a categorization (Fafchamps and Shilpi, 2005). Similarly,
because of the predominance of agricultural activities, differences in the rates of return be-
tween rural areas across regions are likely to arise primarily from the differences in public
capitals and access to markets.
The spatial differences in the rate of return to the same attribute can also be sustained in
an equilibrium if migration is costly (Dahl, 2002; Kanbur and Rapoport, 2005; Bayer, Khan
and Timmins, 2007). The cost of migration tends to vary across individuals and households
as they face different levels of risks and costs. The migration costs are likely to be higher
for the poorer and middle income households who face credit constraint as well as higher
opportunity costs of disposing of existing assets. Various costs associated with migration
are likely to pose no serious hindrance to the mobility of members of well-off households.
Similarly, short-term migrations such as commuting and temporary migration of a member
of the household involve less cost than the long-term and permanent migration of the entire
household. Proximity can also influence the formation of a migration network and through
it, migration flows in subsequent periods (Kanbur and Rapoport, 2005). As a result, the
difference in returns to attributes will be smaller across areas which are in close proximity to
each other.
Both locational sorting and migration literature thus suggests that returns to observed
household attributes will vary across households depending on their position in the welfare
distribution, and across regions depending on their relative proximity and locational char-
acteristics. In this paper, returns to observed household attributes are estimated using the
Machado and Mata (2005) quantile regression based decomposition technique. The estima-
tion is carried out using household level data from two rounds of the Household Expenditure
2
Survey (HIES) (2000 and 2005) of Bangladesh.1 The regional gaps in the welfare in our
empirical analysis are measured by the difference in the distribution of log of real per capita
consumption expenditure between regions. These regional differences in the living standards
are then decomposed into a `sorting' effect arising due to differences in the observed house-
hold characteristics, and a returns effect resulting from the differences in the rates of return
to those characteristics.
Bangladesh provides an excellent case to study the roles of different factors in explain-
ing the spatial differences in returns for several reasons. First, there are no administrative
restrictions on migration in Bangladesh. As much of the Bangladesh's population share the
same ethnicity, religion and language, there are no serious ethnic or cultural barriers to in-
ternal migration. Despite the absence of serious barriers to labor mobility, Ravallion and
Wodon (1999) has shown that both sorting and returns effects are important in explaining
average regional gaps in welfare in Bangladesh.2 Second, the capital city Dhaka and the main
port city Chittagong have emerged as two growth centers in the country, dominating both
the urbanization process and economic growth. The country is sliced into three pieces by
two major Asian rivers, the Ganges and the Brahmaputra. The natural border defined by
these two rivers allows us to define two regions in terms of their access to Dhaka and Chit-
tagong without relying on potentially endogenous factors such as travel time to these centers.
Specifically, we define an integrated (I) region consisting of areas which are geographically
contiguous to either the Dhaka or Chittagong metropolitan areas.3 The rest of the country
constitutes the less integrated (LI) region.4 The natural border created by the rivers hinders
movement of goods and people across the I and LI regions.
1Nguyen et al.(2007) applied this technique to separate out the contribution of covariates and that of
returns to urban-rural inequality in Vietnam.
2Ravallion and Wodon (1999) used data from HIES 1988-89 and 1990-91 and carried out decomposition
exercise based on the mean regressions. Our paper advances the understanding of the spatial differences in
welfare in several important ways. By using the quantile regression approach, we allow household attributes to
have different marginal effects depending on a household's position in income distribution. Moreover, instead
of capturing spatial effects using district dummies, we define regions in terms of differences in infrastructure
endowments and natural borders. Finally, we also attempt to evaluate the contribution of different factors to
the persistence of spatial difference in rates of returns.
3I region thus lies to the North of the Ganges and East of the Brahmaputra rivers.
4The LI region accounts for the territory that lies to the West of the Brahmaputra (Rajshahi Division)
and South of the Ganges (Barisal and Khulna divisions, and a small part of Dhaka Division).
3
The empirical analyses uncover the presence of significant returns differences across re-
gions and across households at a different position in the income distribution. The empirical
results show that changes in the returns effect can explain much of the changes in the I-LI
gap in the distribution of LRPCE between 2000 and 2005. Compared with its levels in 2000,
the differences in the returns effects between the I and LI regions have become larger at the
lower end (below the median) and smaller at the upper end (above the median) of the welfare
distribution. The trends in the returns and covariate effects between 2000 and 2005 indicate
that households at the lower end of the distribution in the LI region have become worse off
not only in terms of their attributes but also in terms of the returns to those attributes.
Households at the upper end of the distribution in the I region, on the other hand, have
become better off in terms of their attributes but experienced a relative decline in returns to
those attributes in 2005. The returns effects across rural areas in the I and LI regions are
substantial for relatively well-off households who face little or no barrier to migration. This
result implies that the differences in market access and public capital are indeed important
in sustaining the regional gaps in welfare. Comparison of the returns effects for the I-LI gap
in rural areas with that for the overall I-LI gaps points to the sorting of households with
better but unobserved attributes in the I region. The downward slope of the returns effect
curve for the I-LI gap in 2005 is consistent with the argument that the poorer households
face an increasing cost of migration. The decomposition of the urban-rural gap in welfare
within each region shows that for the lower half of the distribution, there are virtually no
differences in the returns to household attributes across urban and rural areas. Within each
region, rural-urban migration seems to equalize the returns to household attributes for the
lower quantiles, but even for these quantiles, significant returns differences exist between the
I and LI regions. The evidence thus suggests that physical barriers created by the rivers not
only limit the access to markets but also impose significant migration costs on households
residing in the LI region. The large returns differences observed at the upper end of the
distribution in the case of the urban-rural gaps in welfare is consistent with the theoreti-
cal insights that households with better observed and unobserved attributes, and economic
activities benefiting from the agglomeration economies, often cluster in urban areas.
4
The rest of the paper is organized as the following. Section 2 elaborates the conceptual
framework. Section 3 describes the data used in the analysis. Section 4, organized in a couple
of sub-sections, presents the empirical results. Section 5 concludes the paper.
2 Conceptual Framework
In order to outline the explanations for the spatial gaps in welfare distribution, we start from
a simple adaptation of a locational sorting model developed in Roback (1986). Suppose Vij
represents the indirect utility function of a household i in location j. Following Roback (1986)
and Bayer et al (2006), we specify the indirect utility function as:
Vij = V (Yij;Xij,Aj) (1)
where Yij is the per capita expenditure by household i deflated by cost of living in location
j. Xij is a vector of observed and unobserved household characteristics, and Aj is a vector of
amenities available in location j. If migration is free and cost-less, then in equilibrium, the
following condition will hold:
Vij = V (Yij;Xij,Aj) = Vih = V (Yih;Xih,Ah) = c (2)
where c is a constant. Condition in equation (2) implies that the welfare levels of house-
holds with the same characteristics will be equalized across locations. This means that a high
school graduate household head, ceteris paribus, will earn the same level of welfare regardless
of his or her location. One can still observe higher incidence of poverty in some locations, but
that will be simply because of the concentration of the households with poorer attributes in
those locations. In other words, the welfare differences across locations will be entirely due
to the locational `sorting' of households with different characteristics.
In practice, the indirect utility level enjoyed by a household is not directly observable
and hence in empirical work, Yij is taken as a proxy for the welfare. With Yij indicating the
welfare level, there is now a possibility that returns to high school education, ceteris paribus,
5
can be observed to vary across locations even when the equilibrium condition in equation
(2) holds. To illustrate this possibility, suppose Xij consists of the observed education level
Eij and an unobserved attribute (e.g. ability) ij. Consider the case where two identical
households live in two locations: j with better amenities (e.g. school quality) than h. If
amenity is valued positively by the households, then the equilibrium condition in equation
(2) implies that real wage in location j, w(Xij; Aj) will be lower than that in location h. The
regression of Yij on household characteristics (X) will then suggest lower returns to those
characteristics in the locations with better amenities particularly when the measures of some
of the amenities are unobservable.
Empirical evidence from developing countries, however, suggests lower returns to the
household attributes in areas with weaker infrastructure and amenities (Ravallion and Wodon,
1999; Jalan and Ravallion, 2002). Existing literature offers two possible explanations for the
observed differences in returns across regions. First, even in the presence of cost-less and free
migration, return to the same attribute can be found to vary significantly if the heterogeneity
across households and locations is not adequately controlled for in the econometric estimation.
For instance, locational sorting models a la Roy (1951) suggest that households are sorted
across space in terms of observed and unobserved characteristics. Suppose, real wage in a
location is a function of both observed education level (Eij) and unobserved attribute (ij).
For simplicity, we assume that there is no difference in the amenity across locations and that
for technological reason, activities requiring higher skill and ability are clustered in area h
(e.g. urban area). From the equilibrium condition in equation (2), it follows that:
w(Eij;ij) = w(Ekh;kh) (3)
Since ij < kh, it follows from equation (3) that Eij > Ekh. Because of the geographical
sorting of skill and ability in some locations, for any given education level E, return will be
higher in location h [wh(E) > wj(E)].
Similar to the locational sorting of unobserved attributes, firms are found to cluster
in selected locations because of increasing returns to scale and better access to markets.
As a result of various agglomeration economies, productivity and wages are usually higher
6
in locations with a higher density of population and activities (Venables, 2006). Wage in
this case becomes a function not only of workers observed skills but also of the unobserved
productivity enhancing effect of the clustering of activities. In equation (3), if we interpret
to represent these unobserved externalities, then it becomes clear that the estimates of
returns to observed skills (e.g. education) will be higher in locations with higher density
of skilled workers and activities, and thus lower rate of poverty. Finally, when there is a
positive externality from the local public goods to private production function, then firms
located in areas with better public infrastructure will experience higher productivity (Jalan
and Ravallion, 2002; Ravallion, 2005). Again using equation (3), it can be shown easily
that even with free migration, a typical econometric estimation will provide much higher
estimates of returns to factors in regions with better infrastructure and amenities.5 It should
be noted that the resulting biases in the econometric estimation of returns will not be constant
across all households and locations. Empirical evidence from developing countries shows
that even in urban centers, only a small fraction of the activities use technology that can
generate increasing returns or can internalize benefits from knowledge spill-overs or thick
market externality (Fafchamps and Shilpi, 2005). Similarly, only a small fraction of the
labor force is employed in skilled jobs. Thus sorting of unobserved household characteristics
(e.g. ability) and agglomeration economies are likely to be more relevant for highly skilled
workers who belong to the upper tail of the income distribution.6 Similarly, because of the
predominance of agriculture related work in rural employment, differences between rural areas
across regions are likely to be more due to the differences in infrastructure and other public
goods than in agglomeration economies or ability sorting.
Second, spatial differences in the rates of return may persist when migration is costly.
To see the implication of costly migration, suppose wage for a worker with a given skill is
higher in location h. Let Mijh be the cost of migration for worker i from location j to h. The
5This is because not all of the locational attributes are observable, and even when they are observable,
controlling for all types of locational attributes is not feasible as one quickly runs out of degrees of freedom in
regression (Elison and Gleaser, 1999). In the case of developing countries, data on the state and availability
of local public goods and infrastructure are simply difficult to come by.
6Overman, Rice and Venables (2007) noted that the external benefits associated with thick labor markets
produce clustering mainly of high skilled jobs at selected locations even in developed economies.
7
higher wage in h will trigger migration from j to h until a new equilibrium is reached. The
equilibrium condition with costly migration becomes:
w(Eij) = w(Eih) - Mijh (4)
It follows immediately from equation (4) that wage of a worker will be lower in j compared
with an identical worker in h. Evidence from developing countries suggests that cost of
migration, Mijh, varies across individuals and households. Migration involves risk at the
origin and at the destination. Households may face a shortage of labor due to migration
of its member(s) and there is a uncertainty of securing a job and accommodation at the
destination. A migrant needs a relatively large amount of saving to finance his/her trip
and to sustain him/her during the job search period. While the travel expenses may be of
concern for the poorer households, the phase of unemployment is likely to be much shorter
for them as they engage mainly in unskilled jobs. The migration cost is likely to be high
for middle income households who may face longer waiting period for securing a suitable
job, a disruption in household's economic activities at the origin due to labor shortage and
need to dispose of their existing assets. Various costs associated with migration are likely
to pose no serious hindrance to migration for well-off households.7 Similarly, proximity to
the destination allows temporary migration as well as commuting. The costs of such short-
term migrations are thus much lower than that for the long term migration of the entire
household. By facilitating short-term migrations, proximity can influence the formation of a
migration network and through it, migration flow in the subsequent periods reinforcing the
spatial differences in returns over time (Kanbur and Rapoport, 2006).
Because of differential levels of unobserved heterogeneity and migration costs, the extent
of the returns effect is likely to vary across households depending on its position in the income
distribution, and across regions depending on the feasibility of short-term migration. The
estimation of returns to observed household attributes requires netting out the sorting effect
from the spatial gap in welfare. As a first step to separate out the sorting and returns effects
7In developing countries, Venables (2006) reports that higher skill workers display a significantly higher
propensity to move between locations than their lower skilled counterparts.
8
for the entire distribution of welfare, we use the quantile regression technique to estimate the
following regression for a number of quantiles:
Qq(y|Z,I,U) = q0 + Zq1 + Iq0 + IZq1 + Uq0 + UZq1 + q (5)
where y is the dependent variable and Qq(y|Z,I,U) is the qth conditional quantile of y.
Following Ravallion and Wodon (1999) and Nguyen et al. (2007), we take log of the real per
capita household expenditure as an indicator of welfare (y). The regional gaps in welfare are
measured by the differences in the distribution of the real per capita expenditure between
regions. Z is the matrix of all observable household and locational characteristics other than
the regional dummies. We define two regions in a country (Bangladesh in our empirical
work): an integrated (I) region with better infrastructure and better access to markets, and
a less integrated region (LI) which lacks easy access to markets. The regional dummy I thus
takes the value of one if the location is within the integrated region and zero otherwise. There
are systematic differences in the infrastructure and amenities between the rural and urban
areas regardless of their location in the integrated or less integrated regions. This difference
is captured by an urban dummy (U) in equation (5). The matrices IZ and UZ are matrices
of the interaction of all covariates (Z) with integrated and urban dummies respectively. q is
the regression residual term. q0 is the intercept term, and q1 is the vector of slope coefficients
for the qth quantile for the base region which is rural areas in the LI region. The vectors
q0,q0,q1, and q1 provide the qth quantile intercept and slope differentials associated with
the integrated region and urban areas. Equation (5) is estimated for every quantile in the
set q = {0.01,.02,....,0.99}.
The quantile regression results are then used to carry out the Machado and Mata (2005)
decomposition. Following Machado and Mata (2005) and Nguyen et al (2007), we decompose
the regional welfare gaps into the part that is explained by differences in the distribution
of observable household and locational characteristics (sorting effect) and the part that is
explained by the difference in the distribution of returns to those characteristics (returns
effect). We decompose the gap between the distribution of LRPCE in two arbitrary regions,
Ri and Rj, following the step-wise estimation suggested by Machado and Mata (2005).
9
First, for each quantile q, we estimate the vector of quantile regression coefficients (re-
turns), bi(q), using the data from Ri. Second, using covariates from Rj and vector of coef-
ficients estimated for Ri, we estimate the predicted consumption expenditure as yp(q) = Zj
bi(q) where Zj is the matrix of covariates in Rj. For each quantile q, this generates Nj fitted
values where N is the size of sample for Rj. Third, we select randomly 100 elements of
yp(q) for each q and stack them into a vector yp. This yp is then used to construct the
counter-factual distribution. Now the gap between the qth quantile of LPRCE of the Ri and
Rj can be decomposed as:
yj(q) - yi(q) = [yj(q) - yp(q)] + [yp(q) - yi(q) (6)
Since the counter-factual distribution F(yp) provides the distribution of LRPCE that
would have prevailed if returns to covariates in Rj had been the same as in Ri, the first
term on the right hand side measures the contribution of the difference in returns to the
Ri -Rj gap at the qth quantile. This is known as the returns effect. The second term on the
right hand side, the covariate effect, thus measures the contribution of the different values of
covariates to the Ri - Rj gap at the qth quantile. We generated the confidence intervals of
these effects by randomly re-sampling of the Ri data at the first step of the estimation.
3 Data
The main data source for our empirical analysis is the Household Expenditure Survey (HIES)
2000 and 2005 of Bangladesh which were carried out by the Bangladesh Bureau of Statistics
with assistance from the World Bank. The surveys utilized a nearly identical three-stage
stratified sampling strategy to select a nationally representative sample of the households.
The questionnaires for the two rounds are also nearly identical. The HIES 2000 covers 7440
households in 442 primary sampling units (psus). The sample size for the HIES 2005 is 10,080
households in 504 psus.
Each of the surveys collected a wealth of information on many aspects of the living stan-
dards including detailed household level expenditure, demographics, employment, education,
10
health and remittances. In addition, the detailed community level information on infrastruc-
ture and access to facilities are collected for the rural psus. We utilize these data to construct
both the dependent and explanatory variables. The dependent variable of our empirical
analysis is the log of real per capita household expenditure (LRPCE) measured in 2005
prices. For the purpose of poverty assessment, two separate price indices are defined. They
relate to the "upper" and "lower" poverty lines.8 As the incidence of poverty is estimated
using the upper poverty line, we used price index for the upper poverty line for deflating per
capita expenditure.9
A critical step in the estimation of equation(5) is the identification of the integrated
and less integrated regions. Perhaps because of its smaller geographical size and very high
density of population, Bangladesh does not have a clearly marked "lagging" region, though
the North-West region has been historically known as a region with a higher incidence of
poverty. However, with the spread of irrigated agriculture, the region has become the bread
basket of the country in recent years (Diop, 2005).10
In the context of Bangladesh, metropolitan cities of Dhaka and Chittagong have emerged
as the main growth centers. The urbanization process as well as economic growth in Bangladesh
has been dominated by these metropolitan cities - Dhaka, the capital city with a population
of 10 million and Chittagong, the main port city with a population of 3.4 million. Together
these two cities account for 88 percent of the population in metropolitan areas and 41 percent
of the total urban population. Estimates based on HIES 2000 and 2005 indicate that the
average real per capita income in these cities is about 40 percent higher than that of the
other metropolitan areas. As a result of the higher living standards, Dhaka and Chittagong
cities have acted as magnates for migrants experiencing more than 5 percent growth in pop-
ulation.11 These two cities also act as the main domestic and international trading hubs and
8The upper and lower poverty lines differ in terms of allowances for non-food expenditure. For detail on
the construction of poverty lines, please see Narayan and Yoshida (2007).
9The lower poverty line is used to define the incidence of extreme poverty. It should be noted that for each
year, 16 area specific poverty lines are constructed.
10Despite this progress, there exists still smaller areas with very high incidence of poverty such as the marsh
land.
11The overall rate of population growth is about 1.5 percent according to the Population Census, 2000.
11
are the dominant seat of major administrative and economic functions.
Access to these urban growth centers can be used to define an integrated (I) and a less
integrated (LI) region. One can use some access measures such as travel time to these
cities to identify these regions. However, such measures are arguably endogenous because
of the endogenous placement of road infrastructure. Instead we utilize the natural border
created by two major Asian rivers, the Ganges and the Brahmaputra. These rivers sliced
the country into three pieces (Figure 1). We define the I region as consisting of areas which
are geographically contiguous to either Dhaka or Chittagong metropolitan area. The LI
region on the other hand accounts for the territory that lies to the West of the Brahmaputra
(Rajshahi Division) and South of the Ganges (Barisal and Khulna divisions, and a small
portion of Dhaka division) rivers. The appendix Table A.1 shows that rural areas in the
I and LI regions do not differ substantially in terms of some key infrastructure indicator
(e.g. electricity coverage) except for the presence of different types of banks and distance to
the capital city, Dhaka.12 While there are differences in the urban amenities between these
regions, the most important difference between the I and LI regions is that of the access to
large and growing markets in major metropolitan areas. Because of a significant difference in
the flow of these rivers between the monsoon and dry seasons, unreliable water transportation
and a virtual lack of bridges crossing the rivers,13 year-round commuting for work across the
LI and I is not feasible.
3.1 The Spatial Gaps in Living Standards
In order to provide a feel of the trends in our data, we start with the simple investigation of the
gaps in living standards across regions during 2000 and 2005 in Bangladesh. Figure 2 displays
the difference in LRPCE between the I and LI regions for all of the expenditure quantiles from
5 to 95. In 2000, the I-LI gap in LRPCE has increased with an increase in the consumption
quantiles. For instance, the gap has been about 10.7 percent at the 20th percentile and
19.2 percent at the 80th percentile. This implies that rich in the integrated region has been
12Table A.1 is generated using the community surveys of HES 2000 and 2001. Since these surveys are
conducted only in rural areas, the summary statistics in the table relate only to rural areas.
13The only bridge crossing the Brahmaputra, the Jamuna bridge, had started to operate in 1999.
12
disproportionately better off than their counterparts in the LI region compared with the
extent to which poor in the I region has been better off than their counterparts in the LI
region. The curve showing the I-LI gap in 2005 rotated around the 55th percentile, making
the gap almost flat across all of the expenditure quantiles. Compared with 2000, the I-LI
gap in 2005 has increased for all of the quantiles below the 55th percentile and decreased for
all of the quantiles above the 55th percentile. For instance, the gap is about 16.4 percent at
the 20th percentile and 16.2 percent at the 80th percentile. This implies that the poor (rich)
in the I region has experienced a faster (slower) rate of consumption growth compared with
their counterparts in the LI region.
The I region, being home to two main metropolitan areas in Bangladesh, is more urbanized
than the LI region. One possibility for the observed change in the I-LI gap is that the poor
in the urban areas may have gained more than proportionately in terms of consumption
growth compared with the poor in the rural areas. Figure 3 illustrates the urban-rural gap in
LRPCE in 2000 and 2005. As opposed to the I-LI gap, the urban-rural gap is monotonically
increasing in consumption quantiles for both 2000 and 2005. More importantly, the gap has
shifted downward in 2005 for all consumption quantiles.14 This means that consumption of
the rural population grew at a faster rate compared with urban population in both the I and
LI regions narrowing the urban-rural differences. Despite the narrowing of the urban-rural
differences, the I-LI gap widened at the lower end of the LRPCE distribution.15
4 Empirical Results
For the estimation of equation (5), the vector of explanatory variables Z is constructed using
the household and individual level information collected in the HIES 2000 and 2005. The
demographic effects are controlled in the regression by including household size, the percent-
age of children (less than 13 years) among household members, the age of the household
14A detailed analysis of the urban-rural gaps within each region shows similar downward and parallel shift
in the gaps in 2005 in both regions, though extent of the downward shift was more pronounced in the I region.
15Appendix Table A.2 reports the I-LI and urban-rural gaps in LRPCE from OLS and quantile regression
results for a selected number of quantiles. The results confirm the overall trend observed in Figure 2 and 3.
We omit discussion of the Table for the sake of brevity.
13
head and its squared term, and the gender of the head (female=1) as regressors. The human
capital of the household is measured by four different categories of education of the member
of the household with highest level of education. In addition, we included the number of male
and female household members with education above primary level as separate explanatory
variables. These additional education variables are introduced to capture the role of edu-
cated members of the households other than the person with highest level of education. The
regressions also control for the household's non-liquid assets. This asset variable includes all
types of assets such as house, land, business assets and other durable goods. We include
dummies to indicate household head's main sector (agriculture, manufacturing, services) and
type (private wage employment, self-employment) of employment. We included similar vari-
ables for the household head's spouse but they turn out to be statistically insignificant and
are dropped from the regression. The HIES 2000 and 2005 collected community level infor-
mation for the rural psus. There is, however, no information on the characteristics of the
urban psus, barring us to use these variables as controls. Instead, we include dummies (U=1
or I=1) to capture any mean differences in the overall infrastructure across regions.
Appendix Table A.3 reports summary statistics for the dependent and explanatory vari-
ables for different consumption quantiles for the I and LI regions. A number of variables ex-
hibit interesting patterns. For instance, the percentage of households receiving foreign remit-
tances increases with an increase in per capita real consumption in both regions. Households
own larger amount of assets in the I region relative to the LI region. Similarly agricultural
employment is relatively less important in the I region. The percentage of household head
with education above secondary level increases with an increase in LRPCE. The relation-
ship between LRPCE and education above secondary level is however similar in both I and
LI regions. Overall, the differences between the household characteristics across regions do
suggest presence of some locational sorting of households. We also check the differences in
household attributes across the rural and urban areas (appendix Table A.4). These differ-
ences are relatively larger compared with the I-LI region differences. This suggests a larger
role of locational sorting in explaining the urban-rural differences in welfare.
14
4.1 Quantile Regression Results
Equation (5) is estimated using the quantile regression technique for quantiles 1, 2, ...99.
The standard errors of the estimates are computed using bootstrapping technique (with 500
replications) which corrects for the bias induced by clustering and stratification used in the
sample design. Appendix Table A.5 and A.6 report the detailed regression results for quantiles
5, 25, 50, 75 and 95 for 2000 and 2005 respectively.
The regression results in appendix Table A.5 and A.6 suggest that household character-
istics included in the regression accounts for nearly all of the gaps between the integrated
and less integrated areas in both of the survey years. Out of 99 quantile regressions, the
coefficient of integrated area dummy is statistically significant at the 5 percent level in 10
regressions in 2000 and 20 regressions in 2005.16 The estimated coefficients are smaller
in magnitude in 2005 compared with those of 2000 for all of the quantiles up to the 77th
percentile. Only for quantiles above the 78th percentile, the coefficient of the I dummy is
larger in 2005. This means that some of the I-LI gaps remain unexplained by the covariates
included in the regression for the upper quantiles in 2005. In contrast, the coefficient of the
urban dummy is statistically significant for a number of quantiles for the survey year 2000
but becomes insignificant for all of the quantiles in 2005.
As the covariates can explain much of the I-LI and urban-rural gaps, changes in the
distribution of the covariates and that in their respective returns should be able to explain
the change in the I-LI and urban-rural gaps. Some of the explanatory variables display
interesting patterns across the quantiles and across the survey years. The education level
of the household member with highest level of education is represented in the regression
by four categorical variables: dummies indicating if household member has education up to
primary level, if more than primary but up to secondary level, more than secondary but up
to higher secondary degree, and finally above higher secondary level. For the survey year
2000, the coefficients of the dummies indicating up to higher secondary education are positive
16The quantiles for which the coefficient of I dummy is statistically significant are 61-63, and 70-75 in 2000,
and 64-66, 79-81 and 83-96 in 2005. It should be noted that none of the coefficients are significant at 1 percent
level.
15
and statistically significant for all of the quantiles in the our base case (rural areas in the
LI region), except for the 95th percentile. For 95th percentile, only education above higher
secondary level is statistically significant. The coefficient of dummy for primary education
is significant only in a number of quantiles in 2000. For the survey year 2005, coefficient of
primary education dummy is not statistically significant, and some of the coefficients have
negative signs. Except for some of the lower quantiles, the coefficients of all other education
dummies were positive and statistically significant. In both survey years, the magnitudes of
the coefficients increased across the quantiles and across the education levels. For instance,
for the 75th percentile in 2005, the coefficient of secondary education is 0.09, whereas it is 0.31
for up to higher secondary and 0.41 for above higher secondary education. The coefficients
of interaction of education level with regional dummies indicate a large premium for above
higher secondary education in the urban areas in both survey years. Apart from education,
the household assets have statistically significant positive influence on its expenditure level
in both survey years. The coefficient of the dummy indicating a household receiving foreign
remittance is statistically significant and much larger in magnitude in 2005 compared with
2000. In both survey years, LRPCE is associated significantly negatively with the household
size and the percentage of household members below 13 years of age. For the survey year
2005, several employment variables and dummy for domestic remittances have statistically
significant coefficients, with expected sign.
4.2 The Returns Effect
We decompose the I-LI gaps in the distribution of LRPCE into sorting and returns effects us-
ing the Machado and Mata (2005) technique. Figures 4a and 4b display the sorting/covariate
and returns effects for both survey years for quantiles 5 to 95, with 95 percent confidence
bounds. In 2000, the covariate effect is negative for the lower quantiles (up to the 15th
percentile) and positive for the middle quantiles (45th to 75th percentile). This implies that
households up to the 15th percentile in the LI region had better attributes compared to their
counterparts in the I region in 2000. The reverse is true for households belonging to the
45th to 75th quantiles. For the rest of the quantiles, there were no substantial differences in
16
observed household attributes across the I and LI regions. The covariate effect in 2005 has
shifted upward for all of the quantiles, with upper quantiles experiencing larger magnitude of
the shift. Thus, in 2005, households in the I region have better attributes than those in the
LI region for the entire distribution of LRPCE except for the lowest quantiles up to the 10th
percentile. Such shift in the covariate effect suggests that household level physical and human
capital in the I region has experienced faster growth than that in the LI region. This could
result from the selective migration of households and individuals with superior characteristics
from the LI to I region. This is also possible if proximity to larger urban markets in the I
region has induced faster growth particularly in physical capital. However, if the proximity
to large urban centers is the main reason for the upward shift in the covariate effect, then
one would expect the returns effect to shift upward as well. On the other hand, selective
migration of individuals with better attributes is likely to moderate the differences in returns
to those attributes between the regions.
Figure 4b depicts the returns effect along with its 95 percent confidence bounds. The
returns effect is much larger than the covariate effect for all of the quantiles in 2000. While
the returns effect dominates the covariate effect for the lower quantiles (up to the 50th
percentile), the covariate and returns effects are of similar magnitude for the quantiles above
the median in 2005. The returns effect curve has an upward slope for the higher quantiles
(above the 75th percentile) in 2000. The returns effect curve has rotated in 2005, becoming
downward sloping. Compared with the returns effect in 2000, the differences in returns to
observed household attributes have decreased in 2005 for quantiles above the 41st percentile.
The magnitude of the decrease has been larger at the higher quantiles. In contrast, the
returns effects in 2005 are larger than that in 2000 for all of the quantiles below the 40th
percentile. Moreover, the downward slope of the returns effect curve in 2005 implies that
poorer households in the LI region not only have poorer attributes but also receive much
smaller returns to those attributes compared with their counterparts in the I region. The
changes in the covariate and returns effects between 2000 and 2005 for the upper quantiles
are suggestive of selective migration of individuals with better attributes from the LI to I
region.
17
While the returns effects in 2005 are smaller for the upper quantiles, they are still sub-
stantial in magnitude. For instance, for all of the quantiles above the median, the returns
effects account for about half of the total I-LI gap in LRPCE, the other half explained by
the covariate differences. As already noted, migration costs are unlikely to pose serious im-
pediments to the mobility of households belonging to the upper quantiles. The presence of
substantial returns differences for these households points to the importance of unobserved
locational and household heterogeneity. It should be noted that activities that require bet-
ter (and possibly unobserved) individual attributes, and that are subject to agglomeration
economies are observed to concentrate in the urban areas. Such unobserved heterogeneity is
less important in the rural areas where agriculture -an activity widely believed to be subject
to constant returns to scale -- remains the most important occupation. Thus, any difference
in the returns effect for the relatively well-off households across rural areas in the I and LI
regions are likely to be due to the differences in public capital and access to larger markets.
4.3 Market Access and Public Capital: The I-LI Gap in Rural Areas
In order to assess the role of differences in the public capitals and market access in driving
returns differences across regions, we restrict our sample only to the rural areas across the I
and LI regions. Figure 5 plots the returns effects for the gaps in the distribution of LRPCE
between rural areas in the I and LI regions. While the patterns in Figure 5 are similar to
those found in Figure 4b, the comparison of these two figures shows that for the entire range
of the distribution of LRPCE in 2005, the returns effects for the I-LI gap in rural areas
are smaller in magnitude relative to that for overall the I-LI gap. This points to possible
sorting of households with unobserved but superior attributes in the I region. This is also
indicative of the presence of possible agglomeration forces in the I region. The returns effect
is particularly small in magnitude for the quantiles 91st to 95th (Figure 5). The effect
is nevertheless statistically significant. Even for these quantiles, it explains more than a
third of the I-LI gaps in rural areas. The presence of statistically significant difference in
returns for the upper quantiles of LRPCE across rural areas suggests that overall differences
in infrastructure, access to market and other productive public capitals are important in
18
driving a wedge in the returns to observed household attributes across the I and LI regions.
4.4 Migration Costs, Unobserved Heterogeneity and the Returns Effect
In Figure 4b, the returns effect curve is downward sloping for all of the quantiles below the
40th percentile in both survey years. This implies that differences in the returns to across the
I and LI regions are much larger at the lower end of the LRPCE distribution. The returns
effects curve for 2005 is steeper and lies above that for 2000 suggesting that households at
the lower quantiles in the I region experienced a disproportionately large increase in returns
effect in 2005. The larger returns effect for the lower LRPCE quantiles is consistent with
the argument that the poor may face higher and increasing migration costs. Literature on
migration suggests that costs of short-term and temporary migrations such as commuting or
seasonal migration of a member of a household are lower than that of permanent migration
of the entire household. Proximity to the destination makes commuting and temporary or
short term migrations feasible. Such mobility of workers not only moderates the spatial
differences in returns but also facilitates the formation of a migration network. Any initial
advantage in the formation of a migration network is likely to lead to a greater advantage
in migration in the subsequent periods given the importance of the migration network in
mitigating risk associated migration. Thus households living in the close proximity of the
destination are likely to face much lower costs of migration.17 If migration costs and migration
networks are indeed important in regulating the flows of migrants particularly from the
poorer households, then one would expect the returns effect to be smaller in areas close to
the destinations. As in other developing countries, urban areas remain the most common
destination of migration in Bangladesh. We estimate the returns effect for the urban-rural
gaps in LRPCE distribution in the I and LI regions separately. An advantage of examining
the urban-rural gap is that it can also shed light on the influence of agglomeration economies
and sorting of unobserved household attributes on the returns effects across areas. This is
because the sorting of households with unobserved but superior attributes and the presence
17The main costs of migration for the poor are likley to result from their inability to finance trip(s) to
destination and to tap into the migration network. Proximity to destination has important bearings on both
credit constraint faced by poorer households and formation of migration network.
19
of agglomeration economies are likely to be reflected in the returns differences for the upper
LRPCE quantiles.
Figure 6 displays the returns effect in the case of urban-rural gaps in LRPCE in the I
region. In stark contrast with returns effect in the case of the I-LI gap, there is practically no
difference in the returns to observed households characteristics between the urban and rural
areas in the I region in 2005 for all of the quantiles below the median. For these quantiles,
the returns effect was slightly lower in the urban areas compared with the rural areas in
2000. For all of the quantiles above the median, the returns effect increases with an increase
in LRPCE quantiles in both 2000 and 2005. The slope of the returns effect curve is much
steeper in 2005 compared with that in 2000 implying that households at the upper quantiles
of LRPCE distribution in the urban areas have experienced substantial increase in returns
to their observed attributes. Because of such a large increase in the returns effect for the
upper quantiles, the returns effect in 2005 explained more than half of the urban-rural gaps
in LRPCE for them, whereas it explained about a quarter of the urban-rural gap in 2000.
The large increase in the returns effect for the urban households in the upper quantiles is
consistent with the evidence that main metropolitan areas have experienced higher growth
of economic activities.
Figure 7 displays the trend in the returns effects for the urban-rural gaps in the LI region
which is similar to that in the I region. The returns effects are insignificant for all of the
quantiles below the 60th percentile in both survey years (Figure 7). While the returns effect
increases with an increase in LRPCE for the upper quantiles (above the 60th percentile),
its magnitudes are nearly the same for both survey years. The large returns effects for the
upper end of LRPCE distribution suggest sorting of households with better but unobserved
attributes in urban areas in both the I and LI regions. Such returns differences could also
result from the presence of agglomeration economies in the urban areas.
The evidence in Figure 6 and 7 clearly highlights the absence of substantial returns
differences between the urban and rural areas at the lower end of the distribution within
each region. This implies that for the poorer households, the welfare gap between urban
and rural areas has been primarily due to the sorting effect: rural poorer households have
20
poorer attributes relative to their urban counterparts. More importantly, migration within
each region seems to equalize the returns across urban and rural areas for this part of the
distribution. As the poor typically work in unskilled jobs which have little or no entry barrier,
such convergence of returns across urban and rural areas within each region is expected. More
importantly, the result also indicates that there is practically no serious barrier to mobility
within each region for the poorer households.
Figure 8 plots the returns effect for the gaps in the distribution of LRPCE between urban
areas in the I region and rural areas in the LI region. As opposed to Figure 6 and 7, the
urban-rural differences in the returns are quite substantial for the lower half of the distribution
in both survey years. The results show that migration across the I and LI regions involves
larger costs. The mighty rivers that separate these regions do make temporary migration and
commuting difficult across regions. This barrier to short term migrations combined with its
implications for the formation of a migrant network seems to be responsible for sustaining
and even widening the returns differences between the I and LI regions for the lower half of
the distribution of LRPCE.
5 Conclusions
The spatial inequality in living standards is a fact of life in most developing countries. Empir-
ical evidence from developing countries shows that the rates of return to observable household
attributes vary across location in countries with no apparent restriction on migration. Even
with free factor mobility, such spatial differences in the rates of return can be detected in
the empirical work if households and activities are sorted across locations on the basis of
unobserved attributes. This is also possible if regions differ in terms of local public capitals
with positive externality for the private production, and/or if migration is costly. None of
these factors, however, affects all of the households and all of the locations equally. The
sorting of unobserved attributes is likely to be more important for households belonging to
the upper tail of the welfare distribution, and residing in urban areas. For these households,
migration costs are not likely to restrict their mobility. As agriculture -- an activity which
21
is believed to be subject to constant returns to scale -- predominates in the rural areas, the
differences in the returns for these well-off households located in rural areas would reflect
the differences in the productive public capitals as well as access to markets across regions.
Migration costs affect the mobility of the households belonging to the lower to middle part
of the distribution. As proximity to the destination facilitates short-term migrations and
formation of the migration networks, the returns differences especially for the lower quantiles
are likely to be smaller in magnitude in regions which are close by. In this paper, we examine
the differences in the rates of return to observed household attributes over the entire welfare
distribution and across regions with different levels of infrastructure development, market
access and proximity.
The empirical evidence, based on the quantile decomposition technique pioneered by
Machado and Mata (2005) and on two rounds of household level data from Bangladesh,
uncovers substantial differences in the returns between an integrated (I) region contiguous to
the growth poles (Dhaka and Chittagong metropolitan areas) and a less integrated (LI) region
which is cut-off from the growth poles by two main Asian rivers (Ganges and Brahmaputra).
The returns effect measuring differences in the rates of return to observed household attributes
across rural areas in the I and LI regions is quite substantial for the upper quantiles of the
welfare distribution. This result suggests an important role of the differences in public capitals
and market access in sustaining the differential returns across regions. Comparison of the
returns effects across different areas (rural vs overall I-LI gap) also indicates the sorting of
households with unobserved but better attributes in the I region and in the urban areas
within the I and LI regions respectively. The significant returns effects for the I-LI gap for
the households at the lower end of distribution is consistent with the view that the poor
face higher costs of migration. However, there is virtually no difference in the returns to
observed household attributes across urban and rural areas within each region at the lower
half of the distribution. This result along with the substantial returns effect in the case of the
I-LI gaps imply that while within each region, migration seems to equalize the returns for
the households belonging to the lower half of the distribution, the physical barriers created
by the rivers do impose significant migration costs by hindering short-term migrations and
22
through it, the formation of a migration network.
The empirical results have a number of policy implications. As migration within each
region moderates the differences in the returns to attributes for the poorer households, in-
vestment in enhancing the attributes of these households (e.g. human capital) can contribute
significantly to reducing the gaps in the living standards. Investment in improving connectiv-
ity between the I and LI regions will not only allow better access to markets for the households
in the LI region but also facilitate better flow of migrants across regions. Similarly, invest-
ment in much neglected urban services in the LI region can attract more firms and activities
as well as migrants in the urban centers creating an additional engine of growth within the
LI region.
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25
Figure 1: Main Rivers in Bangladesh
Figure 2: Integrated vs. Less Integrated Region Gap in Log Real Per
Capita Expenditures
0.25
0.2
0.15
0.1
0.05
0
0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
Percentile
2000 2005
Figure 3: Urban-Rural Gaps in Log Real Per Capita Expenditures
0.6
0.5
0.4
0.3
0.2
0.1
0
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
2000 2005
Figure 4a: Covariate Effects for I-LI Gap, 2000 and 2005
95% Confidence Interval
0.1
0.08
E 0.06
C
P
RL 0.04
ni
ecner 0.02
0
ffei 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
D-0.02
-0.04
-0.06
Percentile
2000 2005
Figure 4b: Returns Effects for I-LI Gap, 2000 and 2005
95% Confidence Interval
0.2
0.18
0.16
0.14
0.12
0.1
.
0.08
0.06
0.04
0.02
0
0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
Percentile
2000 2005
Figure 5: Returns Effects for Rural I-Rural LI Gaps, 2000 and 2005
0.25
0.2
E
C
P
RL 0.15
ni
ce 0.1
en
erff
Di 0.05
0
0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
Percentile
2000 2005
Figure 6: Returns Effects for Urban-Rural Gaps in Integrated
Region, 2000 & 2005
0.35
0.3
0.25
E
C
P 0.2
LR
ni 0.15
e
0.1
ncereffi 0.05
D 0
-0.050.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
-0.1
Percentile
2000 2005
Figure 7: Returns Effects for Urban-Rural Gaps in Less
Integrated Region, 2000 & 2005
0.3
0.25
E 0.2
C
P
RL 0.15
ni
0.1
cene 0.05
erffi
D 0
0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
-0.05
-0.1
Percentile
2000 2005
Figure 8: Returns Effects for Urban I - Rural LI Gaps, 2000 & 2005
0.35
E 0.3
PC 0.25
R
L
ni 0.2
ce 0.15
en
0.1
er
ffi
D0.05
0
0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
Percentile
2000 2005
Table A.1: Indicators for Integrated (I) and Less Integrated (LI) Regions
2001 2005
LI I LI I
Head count Ratio (Upper poverty line) 53 46 50 33
Real per capita expenditure 727 800 1046 1207
Electricity in Mouza 67% 63% 80% 83%
BD Krishi Bank in Mouza 7% 17% 27% 45%
Commercial Bank in Mouza 17% 17% 25% 40%
Grameen Bank in Mouza 13% 13% 29% 40%
Market/bazar in Mouza 53% 61% 64% 77%
Distance to thana HQ (km) 10.7 11.1 9.7 15.5
Travel time to thana HQ ('00mins) 0.6 0.7 0.5 0.7
Distance to zila HQ (km) 27.7 33.0 28.6 33.5
Travel time to zila HQ ('00 mins) 1.1 1.2 1.0 2.0
Distance to Dhaka HQ (km) 296.2 169.7 294.4 168.7
Travel time to Dhaka HQ ('00 mins) 4.2 3.0 4.5 3.2
Any banks in Mouza 25% 24% 35% 46%
Source: HES 2000 and 2005
Table A.2 : Estimates of Regional Gaps at the mean and at different quantiles
Year Coefficient Mean Quantiles
2000 5th 25th 50th 75th 95th
Rural-Less Integrated 6.460 5.795 6.156 6.415 6.731 7.282
t-value 705.3 232.2 362.3 351.9 291.4 172.4
Urban 0.25 0.08 0.13 0.24 0.37 0.51
t-value 19.68 2.32 4.12 6.25 8.84 7.31
Integrated 0.12 0.07 0.10 0.13 0.13 0.15
t-value 10.19 0.19 3.88 4.54 3.46 2.66
2005
Rural-Less Integrated 6.831 6.163 6.511 6.775 7.096 7.681
t-value 853.8 384.5 461.3 489.4 389.8 251.5
Urban 0.177 0.007 0.069 0.145 0.280 0.436
t-value 16.424 0.303 3.124 5.761 7.183 7.860
Integrated 0.143 0.153 0.154 0.132 0.138 0.131
t-value 13.487 7.648 7.228 6.060 4.733 2.527
Table A.3a: Definition of Variables
Acronym Definition
RPC Per capita consumption expenditure deflated by regional price index
LRPC Log per capita consumption expenditure deflated by regional price index
I Dummy for integrated region
U Dummy for urban area
drs Dummy =1 if Household Received domestic remittances
frs Dummy =1 if Household Received Remittances from abroad
lasset Log(Total Asset deflated by regional price index)
hsize Household size
hhage Household head's age
hhages Household head's age squared
hhfem Household head Female
hhmar Household head Married
hedu2 HH Head has primary education
hedu3 HH Head has Secondary education
hedu4 HH Head has higher Secondary education
hedu5 HH Head has more than higher Secondary education
pchi Percentage of children
agri agri=1 if HH head employed in agriculture
manu manu=1 if HH head employed in manfacutring
serv serv=1 if HH head employed in services
self self=1 if HH head is self employed
priv priv=1 if HH head employed in private sector wage employment
pmedu Number of adult male with Education equal to or more than primary
pfedu Number of adult female with Education equal to or more than primary
Table A.3- continued: Quintile means of key variables, across integrated and less integrated regions
Consumption Quantiles
Less Integrated Region Integrated Region
Lowest Second Middle Fourth Highest Lowest Second Middle Fourth Highest
Household Expenditure Survey 2000
RPC (Taka) 372.17 514.83 648.38 844.08 1576.17 407.07 577.47 745.60 1016.13 2029.59
LRPC 5.90 6.24 6.47 6.73 7.30 5.99 6.36 6.61 6.92 7.51
drs 0.16 0.19 0.20 0.20 0.25 0.14 0.16 0.21 0.24 0.20
frs 0.01 0.02 0.03 0.05 0.08 0.06 0.10 0.12 0.19 0.21
Lasset 3.37 3.92 4.26 4.83 5.79 3.63 4.22 4.46 4.96 6.01
hsize 1.63 1.55 1.48 1.49 1.45 1.73 1.66 1.56 1.52 1.46
hhage 42.31 42.88 43.46 46.21 47.01 43.37 43.83 43.65 45.43 46.56
hhfem 0.09 0.07 0.05 0.07 0.08 0.11 0.09 0.11 0.12 0.12
hhmar 0.90 0.91 0.91 0.89 0.88 0.90 0.91 0.90 0.89 0.89
hedu2 0.14 0.16 0.16 0.11 0.05 0.13 0.16 0.16 0.12 0.04
hedu3 0.16 0.25 0.28 0.36 0.22 0.18 0.25 0.29 0.31 0.24
hedu4 0.03 0.07 0.07 0.16 0.16 0.02 0.06 0.09 0.14 0.16
hedu5 0.02 0.03 0.08 0.15 0.47 0.01 0.05 0.06 0.19 0.45
pchi 0.48 0.40 0.35 0.29 0.25 0.49 0.43 0.38 0.31 0.26
agri 0.54 0.53 0.46 0.48 0.33 0.44 0.41 0.40 0.31 0.19
manu 0.14 0.13 0.13 0.14 0.15 0.12 0.15 0.17 0.15 0.14
serv 0.23 0.26 0.32 0.28 0.40 0.32 0.34 0.31 0.38 0.47
self 0.31 0.45 0.50 0.58 0.57 0.34 0.42 0.44 0.50 0.44
priv 0.65 0.52 0.47 0.36 0.32 0.56 0.50 0.46 0.36 0.35
pmedu 0.85 0.87 0.95 1.28 1.74 0.95 1.05 0.99 1.32 1.70
pfedu 0.77 0.73 0.73 0.92 1.33 0.84 0.83 0.82 1.05 1.34
Household Expenditure Survey 2005
RPC (Taka) 527.44 727.32 915.39 1217.03 2435.55 619.56 849.66 1061.90 1421.22 2765.78
LRPC 6.25 6.59 6.82 7.10 7.70 6.42 6.74 6.97 7.25 7.85
drs 0.17 0.26 0.26 0.27 0.29 0.14 0.21 0.19 0.21 0.17
frs 0.01 0.01 0.03 0.06 0.12 0.07 0.13 0.15 0.21 0.21
Lasset 2.04 2.68 3.07 3.59 4.40 2.24 2.69 3.13 3.69 4.65
hsize 1.58 1.52 1.44 1.41 1.40 1.68 1.55 1.50 1.46 1.40
hhage 42.79 43.95 45.14 46.53 47.89 42.71 44.99 46.46 46.57 47.41
hhfem 0.10 0.05 0.08 0.09 0.10 0.09 0.10 0.13 0.16 0.16
hhmar 0.88 0.92 0.90 0.90 0.88 0.93 0.91 0.90 0.89 0.89
hedu2 0.34 0.30 0.23 0.17 0.08 0.25 0.24 0.20 0.11 0.05
hedu3 0.28 0.39 0.47 0.51 0.43 0.28 0.39 0.49 0.52 0.39
hedu4 0.01 0.02 0.03 0.08 0.15 0.01 0.01 0.06 0.11 0.16
hedu5 0.01 0.02 0.04 0.10 0.27 0.02 0.02 0.04 0.12 0.34
pchi 0.44 0.39 0.32 0.28 0.24 0.49 0.40 0.34 0.30 0.25
agri 0.39 0.43 0.39 0.36 0.24 0.40 0.37 0.30 0.23 0.13
manu 0.13 0.14 0.14 0.10 0.09 0.14 0.17 0.14 0.14 0.12
serv 0.33 0.33 0.34 0.39 0.47 0.30 0.30 0.36 0.41 0.51
self 0.27 0.39 0.43 0.50 0.50 0.30 0.37 0.40 0.42 0.41
priv 0.63 0.55 0.45 0.36 0.30 0.58 0.48 0.38 0.31 0.28
pmedu 0.11 0.16 0.21 0.28 0.36 0.11 0.16 0.23 0.30 0.38
pfedu 0.11 0.15 0.19 0.24 0.34 0.10 0.16 0.22 0.28 0.39
Table A.4: Quintile means of key variables for Rural and Urban Areas
Consumption Quantiles
Rural Urban
Lowest Second Middle Fourth Highest Lowest Second Middle Fourth Highest
Household Expenditure Survey 2001
RPC (Taka) 378.35 521.15 648.95 833.58 1440.15 413.57 613.69 832.31 1206.92 2433.91
LRPC 5.92 6.25 6.47 6.72 7.21 6.00 6.42 6.72 7.09 7.70
drs 0.15 0.18 0.19 0.22 0.26 0.16 0.20 0.19 0.24 0.16
frs 0.03 0.05 0.07 0.10 0.19 0.03 0.06 0.08 0.11 0.12
Lasset 3.44 3.94 4.34 4.70 5.39 3.65 4.23 4.73 5.47 6.55
hsize 1.66 1.60 1.55 1.51 1.45 1.68 1.57 1.52 1.53 1.45
hhage 42.82 43.54 43.83 45.93 47.46 42.31 42.79 43.40 44.85 46.39
hhfem 0.09 0.08 0.06 0.09 0.11 0.12 0.09 0.08 0.08 0.11
hhmar 0.91 0.91 0.92 0.89 0.87 0.90 0.90 0.89 0.92 0.89
hedu2 0.14 0.16 0.15 0.13 0.09 0.14 0.17 0.12 0.05 0.02
hedu3 0.17 0.23 0.26 0.32 0.30 0.19 0.33 0.34 0.27 0.14
hedu4 0.02 0.06 0.06 0.13 0.14 0.05 0.09 0.16 0.20 0.14
hedu5 0.01 0.03 0.07 0.11 0.28 0.02 0.07 0.14 0.40 0.68
pchi 0.49 0.43 0.37 0.32 0.27 0.46 0.38 0.33 0.28 0.23
agri 0.62 0.60 0.53 0.54 0.47 0.13 0.14 0.14 0.10 0.06
manu 0.10 0.08 0.12 0.11 0.09 0.25 0.25 0.25 0.23 0.15
serv 0.19 0.23 0.25 0.24 0.30 0.50 0.51 0.49 0.50 0.60
self 0.33 0.46 0.50 0.59 0.63 0.31 0.35 0.39 0.36 0.38
priv 0.62 0.49 0.45 0.35 0.27 0.60 0.57 0.48 0.46 0.39
pmedu 0.91 0.90 1.00 1.13 1.43 0.89 1.00 1.30 1.79 1.91
pfedu 0.79 0.76 0.75 0.84 1.07 0.79 0.88 1.02 1.38 1.58
Household Expenditure Survey 2005
RPC (Taka) 551.64 751.80 927.64 1191.28 2168.45 570.78 826.78 1091.48 1563.13 3176.14
LRPC 6.30 6.62 6.83 7.08 7.60 6.33 6.71 6.99 7.35 7.98
drs 0.17 0.24 0.24 0.26 0.26 0.16 0.21 0.21 0.21 0.19
frs 0.02 0.06 0.09 0.14 0.19 0.02 0.05 0.07 0.09 0.15
Lasset 2.22 2.83 3.10 3.49 4.18 1.85 2.52 3.13 4.01 4.88
hsize 1.62 1.56 1.47 1.44 1.39 1.58 1.49 1.44 1.47 1.42
hhage 43.19 44.41 46.16 47.48 49.00 42.22 43.40 44.51 44.94 46.42
hhfem 0.09 0.06 0.10 0.12 0.15 0.10 0.08 0.10 0.10 0.11
hhmar 0.90 0.92 0.90 0.89 0.87 0.90 0.91 0.90 0.91 0.90
hedu2 0.32 0.26 0.24 0.18 0.11 0.31 0.25 0.17 0.07 0.03
hedu3 0.26 0.36 0.43 0.48 0.53 0.33 0.45 0.53 0.49 0.27
hedu4 0.01 0.02 0.02 0.06 0.12 0.02 0.04 0.09 0.15 0.19
hedu5 0.01 0.01 0.03 0.06 0.13 0.02 0.04 0.09 0.25 0.49
pchi 0.46 0.40 0.36 0.31 0.26 0.44 0.35 0.29 0.28 0.25
agri 0.50 0.51 0.47 0.43 0.35 0.19 0.16 0.12 0.07 0.04
manu 0.10 0.10 0.11 0.09 0.08 0.22 0.23 0.19 0.19 0.11
serv 0.26 0.26 0.27 0.29 0.32 0.45 0.47 0.53 0.60 0.64
self 0.29 0.39 0.44 0.50 0.51 0.29 0.36 0.39 0.39 0.39
priv 0.62 0.52 0.42 0.32 0.22 0.61 0.52 0.43 0.41 0.34
pmedu 0.10 0.14 0.18 0.24 0.32 0.14 0.22 0.31 0.38 0.41
pfedu 0.10 0.13 0.17 0.21 0.29 0.12 0.20 0.26 0.36 0.43
Table A.5: Quantile Regression Results :2000
Percentiles
Variables 5th 25th 50th 75th 95th
Coef Z Coef Z Coef Z Coef Z Coef Z
I -0.19 -0.82 0.16 0.96 0.22 1.42 0.33 1.97 0.16 0.54
u 0.03 0.09 0.27 1.35 0.28 1.58 0.00 -0.01 -0.19 -0.55
drs -0.02 -0.57 0.04 1.82 0.01 0.69 0.01 0.33 -0.01 -0.34
frs 0.05 0.53 0.13 2.92 0.13 3.61 0.17 2.89 0.15 1.61
Lasset 0.21 12.52 0.23 21.84 0.24 26.07 0.25 23.73 0.26 17.11
hsize -0.34 -8.34 -0.39 -13.95 -0.47 -18.12 -0.49 -15.95 -0.49 -9.71
hhage -0.07 -1.71 -0.03 -0.94 -0.04 -1.59 -0.03 -1.14 -0.11 -1.61
hhfem -0.02 -0.32 0.01 0.30 -0.02 -0.42 -0.03 -0.82 0.11 1.07
hhmar 0.03 0.56 0.06 1.95 0.04 1.21 0.04 1.21 0.08 1.21
hedu2 0.08 2.88 0.04 1.86 0.06 2.89 0.04 2.12 0.06 1.50
hedu3 0.11 3.40 0.08 3.80 0.09 4.60 0.09 3.85 0.05 1.32
hedu4 0.23 4.34 0.19 7.05 0.15 5.29 0.12 2.82 0.05 0.96
hedu5 0.26 4.28 0.24 7.90 0.28 6.97 0.26 5.77 0.27 3.71
pchi -0.23 -3.24 -0.28 -6.69 -0.24 -5.50 -0.23 -4.89 -0.41 -5.01
agri 0.09 1.19 0.04 0.97 0.03 0.88 0.04 0.69 -0.03 -0.39
manu 0.10 1.24 0.07 1.28 0.07 1.92 0.08 1.43 0.06 0.69
serv 0.13 1.63 0.11 2.30 0.08 2.15 0.12 2.25 0.06 0.84
self -0.01 -0.10 0.02 0.41 -0.02 -0.56 -0.06 -1.03 0.01 0.15
priv 0.00 0.00 0.05 1.10 0.02 0.51 -0.03 -0.59 -0.01 -0.20
pmedu -0.03 -1.91 -0.01 -0.67 0.01 0.75 0.01 1.21 0.03 1.67
pfedu 0.00 0.29 -0.01 -1.13 0.00 0.05 0.01 0.84 0.01 0.85
I*drs 0.10 2.45 0.05 1.94 0.08 3.11 0.07 2.23 0.09 1.75
I*frs 0.06 0.68 0.05 1.01 0.06 1.39 0.02 0.29 0.08 0.84
I*Lasset 0.01 0.30 -0.03 -1.75 -0.03 -1.89 -0.02 -1.18 0.01 0.30
I*hsize 0.02 0.28 -0.01 -0.42 0.01 0.39 0.00 0.06 -0.02 -0.34
I*hhage 0.00 0.00 0.01 0.33 0.00 0.06 -0.03 -0.74 0.03 0.39
I*hhfem 0.08 0.84 0.00 0.00 0.04 0.66 0.04 0.71 -0.19 -1.63
I*hhmar 0.13 1.68 0.00 -0.11 0.02 0.52 0.01 0.22 -0.04 -0.37
I*hedu2 0.00 -0.02 0.05 1.57 0.02 0.65 0.02 0.65 -0.05 -0.88
I*hedu3 -0.03 -0.66 0.05 1.55 0.01 0.17 0.04 1.16 0.00 -0.02
I*hedu4 -0.05 -0.69 0.02 0.49 0.00 0.06 0.03 0.46 0.06 0.76
I*hedu5 0.03 0.39 0.07 1.42 0.00 -0.06 0.07 1.22 -0.03 -0.34
I*pchi -0.08 -0.83 -0.04 -0.70 -0.07 -1.24 -0.02 -0.27 0.00 -0.04
I*agri 0.02 0.17 -0.02 -0.35 -0.02 -0.44 -0.02 -0.32 -0.07 -0.77
I*manu 0.10 1.00 0.03 0.49 0.02 0.38 -0.01 -0.14 -0.05 -0.54
I*serv 0.03 0.30 -0.03 -0.49 0.00 0.00 -0.06 -0.92 -0.07 -0.77
I*self -0.01 -0.08 0.00 0.09 -0.02 -0.43 0.01 0.09 -0.05 -0.75
I*priv 0.04 0.45 0.02 0.37 -0.04 -0.78 -0.01 -0.21 0.01 0.12
I*pmedu 0.02 0.78 0.00 -0.17 0.00 0.16 0.00 -0.27 -0.01 -0.57
I*pfedu 0.01 0.33 0.01 1.15 0.01 0.70 0.00 -0.24 0.03 1.16
u*drs 0.01 0.16 -0.08 -2.71 -0.08 -2.77 -0.04 -1.19 -0.03 -0.58
u*frs 0.05 0.54 -0.03 -0.58 -0.06 -1.26 -0.10 -1.75 -0.12 -1.43
u*Lasset -0.05 -1.91 -0.04 -2.58 -0.03 -1.89 -0.01 -0.81 -0.02 -1.08
u*hsize -0.07 -1.23 -0.14 -3.64 -0.08 -1.93 -0.04 -0.87 -0.17 -2.38
u*hhage 0.06 0.87 0.01 0.19 -0.01 -0.19 0.03 0.48 0.13 1.55
u*hhfem -0.12 -0.98 0.00 0.03 0.01 0.21 0.05 0.92 -0.03 -0.38
u*hhmar 0.00 0.01 0.04 0.75 0.02 0.38 0.04 0.80 -0.03 -0.37
u*hedu2 -0.01 -0.20 0.06 1.42 0.05 1.36 0.04 0.90 -0.02 -0.28
u*hedu3 0.17 3.59 0.14 3.91 0.09 2.36 0.06 1.48 0.13 2.04
u*hedu4 0.08 1.14 0.15 2.82 0.16 2.69 0.16 2.73 0.10 1.08
u*hedu5 0.25 3.66 0.31 5.62 0.23 3.78 0.21 3.19 0.22 2.01
u*pchi -0.04 -0.40 0.09 1.33 0.01 0.21 0.02 0.25 0.27 2.11
u*agri -0.05 -0.48 -0.01 -0.14 0.00 0.04 0.02 0.32 -0.05 -0.41
u*manu -0.11 -0.92 -0.02 -0.28 -0.04 -0.68 -0.01 -0.18 -0.11 -1.05
u*serv -0.07 -0.70 -0.06 -1.03 -0.04 -0.70 0.00 -0.06 -0.07 -0.65
u*self 0.03 0.30 -0.07 -1.10 -0.03 -0.71 -0.04 -0.70 0.03 0.40
u*priv 0.02 0.23 -0.10 -1.67 -0.05 -1.19 -0.05 -1.05 -0.01 -0.13
u*pmedu -0.01 -0.46 0.01 0.87 0.00 0.18 -0.01 -0.32 0.02 0.72
u*pfedu 0.03 1.32 0.02 1.25 0.02 1.50 0.01 0.79 0.01 0.44
Intercept 5.82 36.47 5.88 49.10 6.21 55.48 6.39 54.92 6.92 24.97
Table A.6: Quantile Regression Results :2005
Percentiles
Variables 5th 25th 50th 75th 95th
Coef Z Coef Z Coef Z Coef Z Coef Z
I 0.16 0.71 0.10 0.66 0.22 1.24 0.27 1.60 0.69 2.16
u 0.04 0.17 0.02 0.13 -0.14 -0.82 -0.15 -0.80 -0.49 -1.55
drs 0.10 3.34 0.06 3.76 0.07 3.20 0.06 2.80 0.03 0.73
frs 0.25 4.04 0.31 8.04 0.34 7.14 0.38 6.20 0.65 4.77
Lasset 0.12 8.67 0.10 15.22 0.09 12.75 0.09 11.70 0.11 8.75
hsize -0.35 -7.49 -0.35 -16.48 -0.34 -13.06 -0.37 -12.44 -0.52 -10.82
hhage 0.05 1.24 0.11 4.57 0.08 2.92 0.15 4.69 0.21 3.11
hhfem -0.01 -0.08 -0.01 -0.42 -0.07 -1.56 -0.03 -0.60 -0.11 -1.00
hhmar 0.09 1.64 0.05 1.68 0.03 0.87 0.02 0.69 -0.03 -0.34
hedu2 -0.04 -1.36 0.00 -0.17 0.02 1.40 0.04 2.14 0.08 2.00
hedu3 0.03 0.95 0.05 2.64 0.06 2.78 0.09 3.77 0.17 3.05
hedu4 0.08 1.27 0.20 5.00 0.29 6.15 0.31 5.84 0.43 3.69
hedu5 0.12 1.61 0.23 4.99 0.30 6.81 0.41 5.77 0.52 5.24
pchi -0.27 -3.43 -0.25 -6.84 -0.29 -6.93 -0.26 -5.83 -0.20 -2.18
agri 0.06 0.99 0.04 1.12 0.03 0.91 0.01 0.31 0.14 2.30
manu -0.01 -0.18 0.07 1.61 0.06 1.73 0.09 1.80 0.20 3.34
serv 0.07 1.14 0.08 2.15 0.07 2.32 0.07 1.73 0.25 4.01
self 0.01 0.09 -0.03 -0.71 -0.03 -0.76 -0.02 -0.50 -0.14 -1.74
priv -0.05 -0.75 -0.06 -1.66 -0.13 -3.38 -0.15 -2.93 -0.28 -3.39
pmedu 0.07 1.28 0.11 3.21 0.17 4.39 0.20 4.91 0.26 3.19
pfedu -0.01 -0.09 0.13 3.08 0.18 4.41 0.28 5.90 0.23 2.87
I*drs -0.04 -1.02 -0.05 -1.82 -0.05 -1.62 -0.08 -1.96 -0.01 -0.20
I*frs -0.08 -1.19 -0.20 -4.33 -0.27 -5.02 -0.31 -4.56 -0.56 -3.83
I*Lasset -0.02 -1.54 -0.01 -0.70 0.00 0.16 0.01 1.22 0.03 1.85
I*hsize -0.01 -0.17 -0.06 -2.18 -0.01 -0.46 -0.04 -0.99 0.03 0.44
I*hhage 0.04 0.68 0.02 0.64 -0.03 -0.82 -0.02 -0.40 -0.14 -1.55
I*hhfem -0.01 -0.08 0.06 1.34 0.14 2.84 0.07 1.20 0.08 0.75
I*hhmar -0.06 -1.02 0.03 0.69 0.04 0.85 -0.02 -0.30 -0.04 -0.35
I*hedu2 0.04 0.96 0.06 2.34 0.04 1.59 -0.01 -0.33 -0.07 -1.15
I*hedu3 0.08 1.71 0.06 1.91 0.04 1.33 -0.01 -0.31 -0.14 -1.91
I*hedu4 0.15 1.85 0.13 2.60 0.05 0.88 -0.04 -0.47 -0.08 -0.63
I*hedu5 0.09 1.13 0.06 1.00 0.00 0.01 -0.08 -1.00 0.01 0.08
I*pchi 0.02 0.25 0.05 1.04 0.02 0.32 -0.01 -0.15 -0.07 -0.60
I*agri -0.09 -1.26 -0.08 -1.72 -0.06 -1.31 -0.05 -0.89 -0.06 -0.72
I*manu -0.01 -0.11 -0.02 -0.44 -0.04 -0.81 -0.07 -1.07 -0.09 -0.92
I*serv -0.06 -0.96 -0.05 -1.25 -0.04 -1.03 -0.06 -1.21 -0.12 -1.49
I*self 0.02 0.35 0.05 0.97 0.03 0.67 0.04 0.70 0.05 0.46
I*priv 0.03 0.39 0.02 0.50 0.06 1.35 0.08 1.40 0.10 1.05
I*pmedu 0.01 0.09 0.05 0.94 0.00 -0.07 -0.01 -0.12 -0.23 -2.35
I*pfedu -0.02 -0.22 -0.04 -0.82 -0.04 -0.85 -0.12 -1.75 -0.05 -0.47
u*drs 0.03 0.72 0.02 0.79 -0.02 -0.52 0.03 0.81 0.06 0.96
u*frs -0.03 -0.37 0.00 -0.09 0.05 0.83 0.07 0.98 0.22 1.73
u*Lasset -0.01 -0.33 0.03 2.62 0.04 3.95 0.04 3.58 0.04 2.71
u*hsize -0.04 -0.70 -0.03 -1.23 -0.11 -3.51 -0.11 -2.80 -0.04 -0.54
u*hhage -0.02 -0.37 -0.02 -0.48 0.04 0.98 0.03 0.54 0.11 1.22
u*hhfem 0.01 0.09 -0.03 -0.65 -0.05 -0.95 -0.01 -0.09 0.03 0.27
u*hhmar 0.04 0.41 0.01 0.33 -0.03 -0.53 0.03 0.58 0.04 0.46
u*hedu2 0.09 1.50 0.02 0.53 -0.02 -0.57 0.02 0.44 -0.10 -1.58
u*hedu3 0.06 0.97 0.02 0.45 0.01 0.21 0.04 0.76 -0.05 -0.57
u*hedu4 0.11 1.14 0.01 0.14 -0.04 -0.67 0.11 1.42 -0.11 -0.83
u*hedu5 0.22 2.53 0.21 3.61 0.20 3.19 0.18 1.93 -0.09 -0.71
u*pchi -0.02 -0.24 -0.05 -0.85 0.04 0.69 0.10 1.58 0.10 0.91
u*agri -0.08 -1.03 -0.11 -1.97 -0.11 -2.05 -0.17 -2.66 -0.29 -2.74
u*manu 0.02 0.30 -0.06 -1.04 -0.07 -1.30 -0.09 -1.58 -0.27 -2.54
u*serv -0.01 -0.10 -0.03 -0.57 -0.01 -0.20 -0.05 -1.01 -0.22 -2.41
u*self 0.01 0.12 0.03 0.60 0.04 0.95 0.07 1.22 0.16 1.62
u*priv 0.05 0.71 0.07 1.45 0.11 2.51 0.13 2.34 0.22 2.21
u*pmedu 0.09 1.29 0.03 0.60 0.03 0.58 0.01 0.23 0.14 1.33
u*pfedu 0.11 1.36 0.04 0.83 0.08 1.57 -0.03 -0.44 0.02 0.18
Intercept 6.17 35.24 6.31 60.54 6.67 62.06 6.65 53.73 6.97 26.25